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    <titleInfo>
        <title>Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain</title>
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    <name type="personal">
        <namePart type="given">Alejandro</namePart>
        <namePart type="family">Sierra-Múnera</namePart>
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    <name type="personal">
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        <namePart type="family">Krestel</namePart>
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            <title>Proceedings of the Workshop on Natural Language Processing for Digital Humanities</title>
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        <name type="personal">
            <namePart type="given">Mika</namePart>
            <namePart type="family">Hämäläinen</namePart>
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        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Alnajjar</namePart>
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        <name type="personal">
            <namePart type="given">Niko</namePart>
            <namePart type="family">Partanen</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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    <abstract>Named entity recognition (NER) is an important task that constitutes the basis for multiple downstream natural language processing tasks. Traditional machine learning approaches for NER rely on annotated corpora. However, these are only largely available for standard domains, e.g., news articles. Domain-specific NER often lacks annotated training data and therefore two options are of interest: expensive manual annotations or transfer learning. In this paper, we study a selection of cross-domain NER models and evaluate them for use in the art domain, particularly for recognizing artwork titles in digitized art-historic documents. For the evaluation of the models, we employ a variety of source domain datasets and analyze how each source domain dataset impacts the performance of the different models for our target domain. Additionally, we analyze the impact of the source domain’s entity types, looking for a better understanding of how the transfer learning models adapt different source entity types into our target entity types.</abstract>
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        <date>2021-12</date>
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            <start>173</start>
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%0 Conference Proceedings
%T Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain
%A Sierra-Múnera, Alejandro
%A Krestel, Ralf
%Y Hämäläinen, Mika
%Y Alnajjar, Khalid
%Y Partanen, Niko
%Y Rueter, Jack
%S Proceedings of the Workshop on Natural Language Processing for Digital Humanities
%D 2021
%8 December
%I NLP Association of India (NLPAI)
%C NIT Silchar, India
%F sierra-munera-krestel-2021-enjoy
%X Named entity recognition (NER) is an important task that constitutes the basis for multiple downstream natural language processing tasks. Traditional machine learning approaches for NER rely on annotated corpora. However, these are only largely available for standard domains, e.g., news articles. Domain-specific NER often lacks annotated training data and therefore two options are of interest: expensive manual annotations or transfer learning. In this paper, we study a selection of cross-domain NER models and evaluate them for use in the art domain, particularly for recognizing artwork titles in digitized art-historic documents. For the evaluation of the models, we employ a variety of source domain datasets and analyze how each source domain dataset impacts the performance of the different models for our target domain. Additionally, we analyze the impact of the source domain’s entity types, looking for a better understanding of how the transfer learning models adapt different source entity types into our target entity types.
%U https://aclanthology.org/2021.nlp4dh-1.20
%P 173-182

Markdown (Informal)

[Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain](https://aclanthology.org/2021.nlp4dh-1.20) (Sierra-Múnera & Krestel, NLP4DH 2021)

• Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain (Sierra-Múnera & Krestel, NLP4DH 2021)

ACL

• Alejandro Sierra-Múnera and Ralf Krestel. 2021. Did You Enjoy the Last Supper? An Experimental Study on Cross-Domain NER Models for the Art Domain. In Proceedings of the Workshop on Natural Language Processing for Digital Humanities, pages 173–182, NIT Silchar, India. NLP Association of India (NLPAI).